Axiomatic generalization of the membership degree weighting function for fuzzy C means clustering: Theoretical development and convergence analysis

For decades practitioners have been using the center-based partitional clustering algorithms like Fuzzy C Means (FCM), which rely on minimizing an objective function, comprising of an appropriately weighted sum of distances of each data point from the cluster representatives. Numerous generalizations in terms of choice of the distance function have been introduced for FCM since its inception. In a stark contrast to this fact, to the best of our knowledge, there has not been any significant effort to address the issue of convergence of the algorithm under the structured generalization of the weighting function. Here, by generalization we mean replacing the conventional weighting function uijm (where uij indicates the membership of data xi to cluster Cj and m is the real-valued fuzzifier with 1≤ m < ∞) with a more general function g(uij) which can assume a wide variety of forms under some mild assumptions. In this article, for the first time, we present a novel axiomatic development of the general weighting function based on the membership degrees. We formulate the fuzzy clustering problem along with the intuitive justification of the technicalities behind the axiomatic approach. We develop an Alternative Optimization (AO) procedure to solve the main clustering problem by solving the partial optimization problems in an iterative way. The novelty of the article lies in the development of an axiomatic generalization of the weighting function of FCM, formulation of an AO algorithm for the fuzzy clustering problem with the extension to this general class of weighting functions, and a detailed convergence analysis of the proposed algorithm.